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DepthAnything avatar

DepthAnything/Depth-Anything-V2

0
View on GitHub↗
8,320 stars·862 forks·Python·Apache-2.0·22 viewsdepth-anything-v2.github.io↗

Depth Anything V2

Depth-Anything-V2 is a computer vision foundation model designed for general-purpose spatial understanding and depth perception. It functions as a monocular depth estimation model that predicts relative and absolute depth maps from single images or video sequences.

The project provides specialized tools for both relative depth estimation and metric depth calculation, allowing for the determination of absolute physical distances in indoor and outdoor environments. It includes a video depth estimation framework that ensures temporal consistency across sequential frames to maintain stable depth predictions.

The system utilizes a multi-scale model hierarchy to balance inference speed and accuracy, extracting global context through a transformer-based encoder. Its capabilities cover spatial scene understanding and the export of predicted depth results as grayscale or colorized images.

Features

  • Monocular Depth Estimators - Provides a foundation model that infers three-dimensional spatial depth from single two-dimensional image inputs.
  • Computer Vision Models - Provides a large-scale pre-trained neural network designed for general purpose spatial understanding and depth perception.
  • Depth Estimation - Calculates absolute distance measurements in indoor and outdoor scenes using scale-aware models.
  • Metric - Calculates absolute distance measurements for indoor and outdoor scenes using specialized scale-aware models.
  • Relative - Produces a relative depth map from a single input image using pre-trained foundation models.
  • Temporal Video - Generates depth maps for video sequences while maintaining temporal consistency across frames.
  • Video Depth Frameworks - Provides a framework for generating temporally consistent depth maps across sequential video frames.
  • Relative-to-Metric Depth Scaling - Translates dimensionless relative depth maps into absolute distance measurements using scale-aware model variants.
  • Spatial Understanding - Extracts fine-grained geometric information from images to perceive the layout of a physical space.
  • Metric Depth Mapping - Determines absolute physical distance between the camera and objects in indoor or outdoor environments.
  • Video Depth Analysis - Generates consistent depth maps across video frames to understand the three dimensional structure of moving scenes.
  • Temporal Prediction Smoothing - Ensures depth predictions remain stable and smooth across consecutive video frames to reduce jitter.
  • Transformer Encoders - Uses a vision transformer architecture to extract global context and high-resolution spatial features.
  • Unsupervised Pre-training - Implements unsupervised pre-training on massive unlabeled datasets to learn general depth representations.
  • Model Size Variants - Offers a multi-scale model hierarchy with various parameter counts to balance inference speed and accuracy.

Star history

Star history chart for depthanything/depth-anything-v2Star history chart for depthanything/depth-anything-v2

How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.

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Frequently asked questions

What does depthanything/depth-anything-v2 do?

Depth-Anything-V2 is a computer vision foundation model designed for general-purpose spatial understanding and depth perception. It functions as a monocular depth estimation model that predicts relative and absolute depth maps from single images or video sequences.

What are the main features of depthanything/depth-anything-v2?

The main features of depthanything/depth-anything-v2 are: Monocular Depth Estimators, Computer Vision Models, Depth Estimation, Metric, Relative, Temporal Video, Video Depth Frameworks, Relative-to-Metric Depth Scaling.

What are some open-source alternatives to depthanything/depth-anything-v2?

Open-source alternatives to depthanything/depth-anything-v2 include: liheyoung/depth-anything — Depth-Anything is a monocular depth estimation foundation model that produces dense per-pixel depth maps from a single… bytedance-seed/depth-anything-3 — Depth-Anything-3 is a collection of core model implementations for depth prediction, multi-view geometry estimation,… apple/ml-depth-pro — This project is a monocular depth estimation model and computer vision framework designed to calculate absolute… isl-org/midas — MiDaS is a PyTorch computer vision library and monocular depth estimation model designed to predict scene depth from… vt-vl-lab/3d-photo-inpainting — This project is an RGB-D image inpainting tool and framework for 3D photo reconstruction. It transforms single 2D… baowenbo/dain — DAIN is a video frame synthesis engine and AI video upsampling tool designed to increase video playback smoothness. It…